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Conference Paper: Statistical models for time sequences data mining

TitleStatistical models for time sequences data mining
Authors
KeywordsAutoregression models
Prediction
Clustering
Issue Date2003
PublisherIEEE.
Citation
IEEE International Conference on Computational Intelligence for Financial Engineering Proceedings, Hong Kong, China, 20-23 March 2003, p. 347-354 How to Cite?
AbstractIn this paper, we present an adaptive modelling technique for studying past behaviors of objects and predicting the near future events. Our approach is to define a sliding window (of different window sizes) over a time sequence and build autoregression models from subsequences in different windows. The models are representations of past behaviors of the sequence objects. We can use the AR coefficients as features to index subsequences to facilitate the query of subsequences with similar behaviors. We can use a clustering algorithm to group time sequences on their similarity in the feature space. We can also use the AR models for prediction within different windows. Our experiments show that the adaptive model can give better prediction than non-adaptive models.
Persistent Identifierhttp://hdl.handle.net/10722/48466

 

DC FieldValueLanguage
dc.contributor.authorTing, KWen_HK
dc.contributor.authorNg, KPen_HK
dc.contributor.authorRong, Hen_HK
dc.contributor.authorHuang, JZen_HK
dc.date.accessioned2008-05-22T04:13:59Z-
dc.date.available2008-05-22T04:13:59Z-
dc.date.issued2003en_HK
dc.identifier.citationIEEE International Conference on Computational Intelligence for Financial Engineering Proceedings, Hong Kong, China, 20-23 March 2003, p. 347-354en_HK
dc.identifier.urihttp://hdl.handle.net/10722/48466-
dc.description.abstractIn this paper, we present an adaptive modelling technique for studying past behaviors of objects and predicting the near future events. Our approach is to define a sliding window (of different window sizes) over a time sequence and build autoregression models from subsequences in different windows. The models are representations of past behaviors of the sequence objects. We can use the AR coefficients as features to index subsequences to facilitate the query of subsequences with similar behaviors. We can use a clustering algorithm to group time sequences on their similarity in the feature space. We can also use the AR models for prediction within different windows. Our experiments show that the adaptive model can give better prediction than non-adaptive models.en_HK
dc.format.extent743718 bytes-
dc.format.extent25600 bytes-
dc.format.extent46145 bytes-
dc.format.extent4654 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectAutoregression modelsen_HK
dc.subjectPredictionen_HK
dc.subjectClusteringen_HK
dc.titleStatistical models for time sequences data miningen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailTing, KW: kwting@eti.hku.hken_HK
dc.identifier.emailNg, KP: kkpong@hkusua.hku.hken_HK
dc.identifier.emailRong, H: hrong@eti.hku.hken_HK
dc.identifier.emailHuang, JZ: jhuang@eti.hku.hken_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/CIFER.2003.1196281en_HK
dc.identifier.hkuros76680-

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